<p>
  We conclude that deploying the sentiment analysis strategy on the US drug manufacturing industry does not provide 
  as accurate of results as found by Isah et al (2018). Only after restricting trading to the most profitable day of 
  the week (Berument & Kiymaz, 2001) does the strategy achieve profitability over our testing period. Overall, the 
  strategy produces a Sharpe ratio of 0.116, while the 
  <a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_f14f15cc975842304468786dc56601d7.html">SPY benchmark</a>
  produces a 0.971 Sharpe ratio during the same period. We attribute the decrease in performance to the commissions 
  and spread costs simulated by LEAN.
</p>

<p>
To continue the development of this strategy, future areas of research include:
</p>

<ul>
  <li>Adding a threshold parameter the cumulative sentiment counter must pass to signal trades</li>
  <li>
    Only analyzing the sentiment of articles which contain keywords like "US, "USA", 'Q1', and others in their 
    titles
  </li>
  <li>
    <a href="https://www.datacamp.com/community/tutorials/stemming-lemmatization-python">Stemming</a> and removing 
    punctuation from articles before calculating their sentiment
  </li>
  <li>Adding phrases to the sentiment dictionary or adjust the sentiment values</li>
  <li>Adding other data feeds beside just Tiingo</li>
</ul>